arXiv 2211.080442022-11cs.CV

基于小波变换的遥感数据后门攻击

Backdoor Attacks for Remote Sensing Data with Wavelet Transform

Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；TU Wien（维也纳工业大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗赖贝格资源技术研究所）

Nikolaus Dräger, Yonghao Xu, Pedram Ghamisi

发表信息：: IEEE Trans. Geos. Remote Sens., vol. 61, pp. 1-15, 2023
DOI：: 10.1109/TGRS.2023.3289307

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AI总结：

本文提出基于小波变换的不可见后门攻击 WABA，通过低频域注入触发器，在遥感场景分类和语义分割上实现高攻击成功率。

摘要：

近年来，深度学习算法在地球科学与遥感领域取得了巨大成功。然而，在处理安全攸关的遥感任务时，深度学习模型的安全性和鲁棒性值得特别关注。本文系统分析了针对遥感数据的后门攻击，并同时考虑了场景分类和语义分割任务。现有大多数后门攻击算法依赖可见触发器，例如带有精心设计图案的方形补丁；与之不同，我们提出了一种新颖的基于小波变换的攻击（WABA）方法，该方法通过在低频域将触发器图像注入中毒图像来实现不可见攻击。这样一来，触发器图像中的高频信息会在攻击过程中被滤除，从而实现隐蔽的数据投毒。尽管所提方法简单，但它能够以较高的攻击成功率显著欺骗当前最先进的深度学习模型。我们进一步分析了不同触发器图像以及小波变换中的超参数如何影响所提方法的性能。在四个基准遥感数据集上进行的大量实验证明了所提方法在场景分类和语义分割任务上的有效性，也因此凸显了设计先进后门防御算法以应对遥感场景中这一威胁的重要性。代码将在 https://github.com/ndraeger/waba 在线提供。

英文摘要：

Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical remote sensing tasks. In this paper, we provide a systematic analysis of backdoor attacks for remote sensing data, where both scene classification and semantic segmentation tasks are considered. While most of the existing backdoor attack algorithms rely on visible triggers like squared patches with well-designed patterns, we propose a novel wavelet transform-based attack (WABA) method, which can achieve invisible attacks by injecting the trigger image into the poisoned image in the low-frequency domain. In this way, the high-frequency information in the trigger image can be filtered out in the attack, resulting in stealthy data poisoning. Despite its simplicity, the proposed method can significantly cheat the current state-of-the-art deep learning models with a high attack success rate. We further analyze how different trigger images and the hyper-parameters in the wavelet transform would influence the performance of the proposed method. Extensive experiments on four benchmark remote sensing datasets demonstrate the effectiveness of the proposed method for both scene classification and semantic segmentation tasks and thus highlight the importance of designing advanced backdoor defense algorithms to address this threat in remote sensing scenarios. The code will be available online at \url{https://github.com/ndraeger/waba}.

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